A machine of any kind takes time to be accustomed of, which can be termed as the learning curve for both the machine and its operator and the troubles faced by the operator during this time are called as the teething problems. During this stage, while the operator gets used to the nuances of practical usage of the machine, the machine also gets used to the practical applications of itself.

History

In the digital world machine learning is quite different than the physical world. In 80s and 90s and till the first decade of 21st century machine learning was often associated with the cognitive learning and almost always implemented using very sophisticated algorithms through neural network or at least AI models. (As late as in the mid of nineties Fuzzy Logic had created quite a stir in this area.) At that time, the PC evolution was at a very nascent stage and the computing power of the computers was very limited and therefore the high-end algorithms envisaged during those days, either used to remain unimplemented or were tried using the few supercomputers developed across the world.

Evolution

With the advent of Internet and wireless technologies and the revolution in RAM storage capacities, there was a sudden surge in the computational power of the computers and now we see complex phenomena like voice and image analysis made very simple. With this, the machine learning, which, once upon a time was only for the sophisticated developers arena, has become everyone’s buzzword.

However, considering the market and business orientation of the technology, the contemporary machine learning has been limited to data and pattern only and not high-end cognitive learning as imagined earlier. Even the most sophisticated robots of today’s world don’t go into weaving dreams or genetic analysis and their maximum application remains within the weather forecasting and aerodynamic calculation and in the apparel and entertainment industry.

In the contemporary view, the machine learning is of the following kinds as described below. (Certain desired action follows the mapping.)

Kind

Description

Comments

Example

·Supervised

Provide input and output; objective is to map input to output.

Simple programs

o Semi-supervised

Provide partial input and partial output; objective is to complete the input and output as necessary and then map.

Yearly financial analysis (when a head for month or department is missing, the analysis blanks the head and moves on), Inventory stock checking (any item missing in the rack would create a blank space holder in the report)

o Active

Provide output or input and interactively present the input and output; objective is to map input with output

Warehouse stock update (stocks are provided upon prompted), medical advice (answered upon questioning), recipe control in food processing (amount or any other parameter is filled on the go)

o Reinforcement

Input is given only when required and as part of the reaction to the observation of performance; objective is to guess the input and output and map

Flight autopilot, vehicle driving, playing games, coloring a picture

·Unsupervised

No input; objective is to guess the input from output and map accordingly

Cognitive

Robots as hotel receptionists and program anchors

Case:

Objective – Yearly report to be produced in multiple threads (a single thread would take longer)

Semi-supervised learning – Let the program analyze the data in each column and come up with the thread identifiers and then constitute the data for each thread. The risk here is, unless any guidance given for the thread size, the threads could be of dissimilar in size defeating the purpose.

Unsupervised learning– Let the program analyze the optimum thread size and then analyze the data in each column and come up with the thread identifiers and then constitute the data for each thread.

Examples of Machine learning in SAP EAM environment:

Serialization

Supervised – Serialization based on given criteriaSemi-Supervised – Serialization based on past experience

Order and notification type determination

Supervised – Flags supplied as part of the data from the interfacesActive – Based on the device acting as data source